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Project

Deep Restricted Kernel Machines: Methods and Foundations

This research proposal entitled "Deep Restricted Kernel Machines: Methods and Foundations" is related to two main directions in the field of machine learning:

  • deep learning
  • support vector machines and kernel methods

This project aims at an in-depth study of the recently proposed "Deep Restricted Kernel Machines" (Deep RKM). A method of conjugate feature duality is used to obtain a representation in terms of visible and hidden units. In this way the class of restricted kernel machines can be linked to restricted Boltzmann machines, which do not contain hidden-to-hidden connections. Deep RKM is obtained by coupling the restricted kernel machines over different levels.

The main objectives of the proposal are

  • to investigate the duality principles
  • to extend the class of restricted kernel machine models
  • to explore different coupling schemes and obtain efficient learning rules
  • to develop methods for large scale problems and big data.

The project intends to achieve a new powerful class of machine learning techniques for supervised, unsupervised and semi-supervised learning, and contribute to setting new foundations both for deep learning and for support vector machines and kernel methods.

Date:1 Jan 2017 →  31 Dec 2020
Keywords:Deep Restricted Kernel Machines
Disciplines:Manufacturing engineering, Other mechanical and manufacturing engineering, Product development